While the original dataset includes 74,099 tweets, we are only concerned with the tweets that occur within the first 6 hours of the fire starting. This ends up being just 446 tweets, which amounts to 0.60% of the overall data. Of these we have 425 (95.51%) classified as public tweets and 21 (4.71%) classified as media tweets (tweets from our local media accounts). Our explanatory variable is defined as the local media accounts.
Our response measure is the time of the tweets as we are interested in the difference in time between real life events and tweets. Here we show the overall timeline of the first six hours of tweets that we will be referencing throughout.
| Time of First Tweet |
|---|
| 2018-11-08 06:51:47 |
The response times and amount of responses for each account can be shown here to display the accounts that were most involved within the first hours of the fire. The media’s first response to the fire on Twitter appears at 06:51:47 by CAL FIRE Butte County
| User | Time of First Tweet |
|---|---|
| ButteSheriff | 2018-11-08 07:23:02 |
| CALFIRE_ButteCo | 2018-11-08 06:51:47 |
| ChicoFD | 2018-11-08 10:46:27 |
Here we can see the activity of public accounts to see the frequency of tweets. What’s important to note is the vast number of retweets, showing the number of tweets that are typically dispersing the media’s tweets to reach a wider audience.
The first tweet by a public user closely followed CAL FIRE Butte County’s at 06:54:55
In the following timeline each dot signifies a tweet or event to show tweet times in comparison to the real event times. The majority of the tweets shown are evacuation warnings and orders.
After eliminating retweets the following timeline compares the tweet frequency of the public vs the media for the morning up until 10:00 AM.
Cottle, Simon. (2014). Rethinking Media and Disasters in a Global Age: What’s Changed and Why It Matters. Media, War & Conflict. 7. 3-22.10.1177/1750635213513229. https://www.researchgate.net/publication/270633894_Rethinking_Media_and_Disasters_in_a_Global_Age_What's_Changed_and_Why_It_Matters
Elliott, Jennifer. “8 Best Practices for Emergency Communications on Social Media.” EfficientGov, 19 July 2018, https://www.efficientgov.com/community-engagement/articles/8-best-practices-for-emergency-communications-on-social-media-vwZS7OO5eoblrs3G/.
Epley, Robin (November 8, 2019). “Timeline: Breaking down Nov. 8 - the Day the Camp Fire Sparked.” Chico Enterprise-Record, Chico Enterprise-Record https://www.chicoer.com/2019/11/07/timeline-breaking-down-nov-8-the-day-the-camp-fire-sparked/.
Ford, Jordan. “Improving Disaster Response through Twitter Data.” Penn State University, 2018, https://news.psu.edu/story/527730/2018/07/10/research/improving-disaster-response-through-twitter-data.
Kearney MW (2019). “rtweet: Collecting and analyzing Twitter data.” Journal of Open Source Software, 4(42), 1829. Doi: 10.21105/joss.01829, R package version 0.7.0, https://joss.theoj.org/papers/10.21105/joss.01829.
R Core Team (2019). R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org.
Radio Ink (March 3, 2020). “The Decline of the Home Radio”. https://radioink.com/2020/03/03/the-decline-of-the-home-radio/
Rice, Doyle (January 8, 2019). “USA had world’s 3 costliest natural disasters in 2018, and Camp Fire was the worst”. USA Today.
St. John, Paige, and Joseph Serna. “Camp Fire Evacuation Warnings Failed to Reach More than a Third of Residents Meant to Receive Calls.” Los Angeles Times, 1 Dec. 2018, https://www.latimes.com/local/california/la-me-ln-paradise-evacuation-warnings-20181130-story.html.
Stirtz, Kevin. “Twitter Ranking: Which States Twitter the Most?” All Business, Dun & Bradstreet, 14 May 2009, https://www.allbusiness.com/twitter-ranking-which-states-twitter-the-most-12329567-1.html.